Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association

Nan Xu, Zhixiong Zeng, Wenji Mao


Abstract
Sarcasm is a sophisticated linguistic phenomenon to express the opposite of what one really means. With the rapid growth of social media, multimodal sarcastic tweets are widely posted on various social platforms. In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions. Thus traditional text-based methods are insufficient to detect multimodal sarcasm. To reason with multimodal sarcastic tweets, in this paper, we propose a novel method for modeling cross-modality contrast in the associated context. Our method models both cross-modality contrast and semantic association by constructing the Decomposition and Relation Network (namely D&R Net). The decomposition network represents the commonality and discrepancy between image and text, and the relation network models the semantic association in cross-modality context. Experimental results on a public dataset demonstrate the effectiveness of our model in multimodal sarcasm detection.
Anthology ID:
2020.acl-main.349
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3777–3786
Language:
URL:
https://aclanthology.org/2020.acl-main.349
DOI:
10.18653/v1/2020.acl-main.349
Bibkey:
Cite (ACL):
Nan Xu, Zhixiong Zeng, and Wenji Mao. 2020. Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3777–3786, Online. Association for Computational Linguistics.
Cite (Informal):
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association (Xu et al., ACL 2020)
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PDF:
https://aclanthology.org/2020.acl-main.349.pdf
Video:
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